Naren Sengodan

Naren Sengodan

Breast cancer histopathology image classification is critical for early detection and improved patient outcomes. 1 This study introduces a novel approach leveraging EfficientNetV2 models, to improve feature extraction and focus on relevant tissue regions. The proposed models were evaluated on the BreakHis dataset across multiple magnification scales (40X, 100X, 200X, and 400X). 2 Among them, the EfficientNetV2-XL with CBAM achieved outstanding performance, reaching a peak accuracy of 99.01 percent and an F1-score of 98.31 percent at 400X magnification, outperforming state-of-the-art methods. 3 By integrating Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and optimizing computational efficiency, this method demonstrates its suitability for real-time clinical deployment. 3 The results underscore the potential of attention-enhanced scalable architectures in advancing diagnostic precision for breast cancer detection.

Naren Sengodan

Fine-Grained Visual Classification (FGVC) is a critical and challenging area within computer vision, demanding the identification of highly subtle, localized visual cues. The importance of FGVC extends to critical applications such as biodiversity monitoring and medical diagnostics, where precision is paramount. While large-scale Vision Transformers have achieved state-of-the-art performance, their decision-making processes often lack the interpretability required for trust and verification in such domains. In this paper, we introduce The Loupe, a novel, lightweight, and plug-and-play attention module designed to be inserted into pre-trained backbones like the Swin Transformer. The Loupe is trained end-to-end with a composite loss function that implicitly guides the model to focus on the most discriminative object parts without requiring explicit part-level annotations. Our unique contribution lies in demonstrating that a simple, intrinsic attention mechanism can act as a powerful regularizer, significantly boosting performance while simultaneously providing clear visual explanations. Our experimental evaluation on the challenging CUB-200-2011 dataset shows that The Loupe improves the accuracy of a Swin-Base model from 85.40% to 88.06%, a significant gain of 2.66%. Crucially, our qualitative analysis of the learned attention maps reveals that The Loupe effectively localizes semantically meaningful features, providing a valuable tool for understanding and trusting the model's decision-making process.